Author Info
Name
Affiliation
Papers
WEI-QI WEI
Institute for Health Informatics, University of Minnesota, Twin Cities, MN, United States
31
Collaborators
Citations 
PageRank 
179
137
16.54
Referers 
Referees 
References 
603
266
77
Search Limit
100603
Title
Citations
PageRank
Year
ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes10.342021
Ddiwas: High-Throughput Electronic Health Record-Based Screening Of Drug-Drug Interactions10.352021
Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort10.342021
Genomic considerations for FHIR®; eMERGE implementation lessons00.342021
A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-1910.342021
Phemap: A Multi-Resource Knowledge Base For High-Throughput Phenotyping Within Electronic Health Records10.352020
Combining Publicly-Available and Electronic Health Record Data to Reposition Drugs.00.342019
Detecting Time-Evolving Phenotypic Topics via Tensor Factorization on Electronic Health Records: Cardiovascular Disease Case Study.10.352019
Making work visible for electronic phenotype implementation: lessons learned from the eMERGE network.00.342019
Facilitating phenotype transfer using a common data model.20.642019
Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43, 870 individuals from the eMERGE network.00.342019
Deep Learning Using Electronic Health Records and Genetic Data to Predict Cardiovascular Diseases.00.342019
Using Topic Modeling to Identify Relationship between LPA Variant and Disease Phenotypes.00.342018
Characterizing Design Patterns Of Ehr-Driven Phenotype Extraction Algorithms00.342018
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.90.552017
Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.100.552016
Quantifying Tobacco Exposure Using Clinical Notes and Natural Language Processing to Enable Lung Cancer Screening.00.342015
PheWAS Network Analysis and Visualization.00.342015
Evaluation of Diagnosis Codes, Clinical Notes, and Medications on Identifying Subjects with a Specific Disease Phenotype.00.342014
The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.40.522013
Using PheWAS and Natural Language Processing to Discover Clinical Associations for Congenital Chest Deformities.00.342013
Validation and enhancement of a computable medication indication resource (MEDI) using a large practice-based dataset.50.502013
Development and evaluation of an ensemble resource linking medications to their indications.220.862013
Analyzing Differences between Chinese and English Clinical Text: A Cross-Institution Comparison of Discharge Summaries in Two Languages60.482013
Terminology representation guidelines for biomedical ontologies in the semantic web notations.50.512013
Classifying ICD-9 codes into meaningful disease categories: A comparison between two coding systems.00.342013
Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.523.572012
Diabetes and Susceptibility to Infection: A Study of Lab Culture Results in the EMR.00.342012
Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus.161.192012
Comparing Diagnoses Recorded in Problem Lists vs. Administrative Codes.00.342012
Time-Oriented Question Answering from Clinical Narratives Using Semantic-Web Techniques00.342010